IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0269192.html
   My bibliography  Save this article

Cost-utility analysis of adding abiraterone acetate plus prednisone/prednisolone to long-term hormone therapy in newly diagnosed advanced prostate cancer in England: Lifetime decision model based on STAMPEDE trial data

Author

Listed:
  • Caroline S Clarke
  • Rachael M Hunter
  • Andrea Gabrio
  • Christopher D Brawley
  • Fiona C Ingleby
  • David P Dearnaley
  • David Matheson
  • Gerhardt Attard
  • Hannah L Rush
  • Rob J Jones
  • William Cross
  • Chris Parker
  • J Martin Russell
  • Robin Millman
  • Silke Gillessen
  • Zafar Malik
  • Jason F Lester
  • James Wylie
  • Noel W Clarke
  • Mahesh K B Parmar
  • Matthew R Sydes
  • Nicholas D James

Abstract

Adding abiraterone acetate (AA) plus prednisolone (P) to standard of care (SOC) improves survival in newly diagnosed advanced prostate cancer (PC) patients starting hormone therapy. Our objective was to determine the value for money to the English National Health Service (NHS) of adding AAP to SOC. We used a decision analytic model to evaluate cost-effectiveness of providing AAP in the English NHS. Between 2011–2014, the STAMPEDE trial recruited 1917 men with high-risk localised, locally advanced, recurrent or metastatic PC starting first-line androgen-deprivation therapy (ADT), and they were randomised to receive SOC plus AAP, or SOC alone. Lifetime costs and quality-adjusted life-years (QALYs) were estimated using STAMPEDE trial data supplemented with literature data where necessary, adjusting for baseline patient and disease characteristics. British National Formulary (BNF) prices (£98/day) were applied for AAP. Costs and outcomes were discounted at 3.5%/year. AAP was not cost-effective. The incremental cost-effectiveness ratio (ICER) was £149,748/QALY gained in the non-metastatic (M0) subgroup, with 2.4% probability of being cost-effective at NICE’s £30,000/QALY threshold; and the metastatic (M1) subgroup had an ICER of £47,503/QALY gained, with 12.0% probability of being cost-effective. Scenario analysis suggested AAP could be cost-effective in M1 patients if priced below £62/day, or below £28/day in the M0 subgroup. AAP could dominate SOC in the M0 subgroup with price below £11/day. AAP is effective for non-metastatic and metastatic disease but is not cost-effective when using the BNF price. AAP currently only has UK approval for use in a subset of M1 patients. The actual price currently paid by the English NHS for abiraterone acetate is unknown. Broadening AAP’s indication and having a daily cost below the thresholds described above is recommended, given AAP improves survival in both subgroups and its cost-saving potential in M0 subgroup.

Suggested Citation

  • Caroline S Clarke & Rachael M Hunter & Andrea Gabrio & Christopher D Brawley & Fiona C Ingleby & David P Dearnaley & David Matheson & Gerhardt Attard & Hannah L Rush & Rob J Jones & William Cross & Ch, 2022. "Cost-utility analysis of adding abiraterone acetate plus prednisone/prednisolone to long-term hormone therapy in newly diagnosed advanced prostate cancer in England: Lifetime decision model based on S," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-17, June.
  • Handle: RePEc:plo:pone00:0269192
    DOI: 10.1371/journal.pone.0269192
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0269192
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0269192&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0269192?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    3. Don Husereau & Michael Drummond & Stavros Petrou & Chris Carswell & David Moher & Dan Greenberg & Federico Augustovski & Andrew Briggs & Josephine Mauskopf & Elizabeth Loder, 2013. "Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(3), pages 367-372, June.
    4. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    5. Nicholas R. Latimer, 2013. "Survival Analysis for Economic Evaluations Alongside Clinical Trials—Extrapolation with Patient-Level Data," Medical Decision Making, , vol. 33(6), pages 743-754, August.
    6. Putter, Hein, 2011. "Special Issue about Competing Risks and Multi-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i01).
    7. Anirban Basu & Andrea Manca, 2012. "Regression Estimators for Generic Health-Related Quality of Life and Quality-Adjusted Life Years," Medical Decision Making, , vol. 32(1), pages 56-69, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saha, Sanjib & Gerdtham, Ulf-G. & Toresson, Håkan & Minthon, Lennart & Jarl, Johan, 2018. "Economic Evaluation of Nonpharmacological Interventions for Dementia Patients and their Caregivers - A Systematic Literature Review," Working Papers 2018:10, Lund University, Department of Economics.
    2. Geronimi, J. & Saporta, G., 2017. "Variable selection for multiply-imputed data with penalized generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 103-114.
    3. Rachel Elliott & Koen Putman & Matthew Franklin & Lieven Annemans & Nick Verhaeghe & Martin Eden & Jasdeep Hayre & Sarah Rodgers & Aziz Sheikh & Anthony Avery, 2014. "Cost Effectiveness of a Pharmacist-Led Information Technology Intervention for Reducing Rates of Clinically Important Errors in Medicines Management in General Practices (PINCER)," PharmacoEconomics, Springer, vol. 32(6), pages 573-590, June.
    4. Yates, Brian T., 2021. "Toward collaborative cost-inclusive evaluation: Adaptations and transformations for evaluators and economists," Evaluation and Program Planning, Elsevier, vol. 89(C).
    5. Deidda, Manuela & Geue, Claudia & Kreif, Noemi & Dundas, Ruth & McIntosh, Emma, 2019. "A framework for conducting economic evaluations alongside natural experiments," Social Science & Medicine, Elsevier, vol. 220(C), pages 353-361.
    6. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    7. Saha, Sanjib & Gerdtham, Ulf-G. & Toresson, Håkan & Minthon, Lennart & Jarl, Johan, 2018. "Economic Evaluation of Interventions for Screening of Dementia," Working Papers 2018:20, Lund University, Department of Economics.
    8. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    9. Wei Pan, 2001. "Model Selection in Estimating Equations," Biometrics, The International Biometric Society, vol. 57(2), pages 529-534, June.
    10. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    11. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    12. Najmiatul Fitria & Antoinette D. I. Asselt & Maarten J. Postma, 2019. "Cost-effectiveness of controlling gestational diabetes mellitus: a systematic review," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 407-417, April.
    13. Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    14. Thomas Grochtdreis & Hans-Helmut König & Alexander Dobruschkin & Gunhild von Amsberg & Judith Dams, 2018. "Cost-effectiveness analyses and cost analyses in castration-resistant prostate cancer: A systematic review," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-25, December.
    15. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    16. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    17. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    18. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    19. Michael S. Rendall & Bonnie Ghosh-Dastidar & Margaret M. Weden & Zafar Nazarov, 2011. "Multiple Imputation for Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys," Working Papers WR-887-1, RAND Corporation.
    20. J M van Niekerk & M C Vos & A Stein & L M A Braakman-Jansen & A F Voor in ‘t holt & J E W C van Gemert-Pijnen, 2020. "Risk factors for surgical site infections using a data-driven approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0269192. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.